Transcription factors (TFs) are proteins that control the rate of transcription. They are main regulators of gene transcription. Knowing their targets is very important for understanding developmental processes, cellular stress response and genetic causes of disease. Most of prokaryotic genome is coding and TF binding sites are usually close to genes. However, for the mammalian system, most of its genome is non-coding and TFs usually bind to gene distal regions and they regulate gene transcription via chromosome looping. In our study, we were trying to identify TF targets in both the simple prokaryotic system and the complex mammalian system by integrative omics data analysis. Considering the differences between prokaryotic and mammalian systems, we integrated different omics data in each system to identify TF targets. In prokaryotes, DNA is organized in operon which contains a cluster of genes under the control of a single promoter. There is stronger correlation between TF binding and gene expression in prokaryotes than in the mammalian system. And TF motif in prokaryotes is usually longer and more speciﬁc than that in eukaryotes. Therefore, in prokaryotes, we integrated TF genome-wide binding data, expression data and motif information to identify TF targets. We conducted our study using TF NsrR and tried to identify its genome-wide binding targets in Uropathogenic Escherchia coli (UPEC) CFT073 to understand UPEC’s response to nitric oxide. In the mammalian system, DNA is wrapped on histone to form nucleosome. Histone modiﬁcation and chromatin accessibility are important for transcription factor binding. DNA can form looping interactions to regulate gene expression. Therefore for TF targets identiﬁcation in the mammalian system, we integrated TF genome-wide binding data, epigenetic data and chromatin looping interaction data. We built a classiﬁer to predict TP53-associated looping interactions and genome-wide long-distance targets of TP53.